Download Using data mining to detect insurance fraud

IBM Software
Business Analytics
IBM® SPSS® Modeler
Using data mining to
detect insurance fraud
Improve accuracy and minimize loss
Introduction
Highlights:
•
IBM SPSS Modeler combines powerful
analytical techniques with existing
fraud detection and prevention efforts
•
Build models based on previously
audited claims and use them to identify
potentially fraudulent future claims
•
Ensure your adjusters focus on claims
that are likely to be fraudulent and have
the greatest potential for adjustment
•
Deploy results to the people who can
use that information to eradicate fraud
and recoup money
Every organization that exchanges money with customers, service
providers or vendors risks exposure to fraud and abuse.
Insurance companies around the world lose more and more money
through fraudulent claims each year. They need to recoup this lost
money so they can continue providing superior services for their
customers.
IBM SPSS data mining tools data mining tools are based on industry
standards allowing agencies to combine IBM SPSS data mining with
existing fraud detection and prevention efforts to improve accuracy,
decrease manpower and minimize loss. The combined effort of IBM
and SPSS brings you the utmost in flexibility in the kinds of data you
mine and how you deploy results.
To ensure adjusters target claims which have the greatest likelihood of
adjustment, many insurance companies have incorporated data mining
into their investigating and auditing processes. Data mining combines
powerful analytical techniques with your firsthand business knowledge
to turn data you’ve already acquired into the insight you need to
identify probable instances of fraud and abuse.
SPSS was one of the pioneers in the field of data analysis; it was first on
the scene and continues to be one of the most popular and widely used
software applications. As a new member of the IBM organization, SPSS
brings its leading-edge analytic tools to a broader number of customers
worldwide.
IBM SPSS offerings include industry-leading products for data
collection, statistics and data mining, with a unifying platform
supporting the secure management and deployment of analytical assets.
IBM Software
Business Analytics
IBM SPSS Modeler
Discover how to prevent loss due to fraud
Summary:
Insurance companies lose millions of
dollars each year through fraudulent
claims, largely because they do not have a
way to easily determine which claims are
legitimate and which may be fraudulent.
To ensure that adjusters target claims
which have the greatest likelihood of
adjustment, many insurance companies
have incorporated IBM SPSS data mining
into their investigating and auditing
processes. This report describes how
data mining techniques can enable you to
improve accuracy and save time, money
and resources.
How does your organization determine which of its thousands or
millions of claims are legitimate? Perhaps your adjusters tend to target
claims or payment requests that represent inconsequential adjustments,
while missing the ones that offer significant amounts of money to
recoup. What if you could:
•
•
•
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Discover small subsets of claims with a high percentage of recoverable
fraud?
Isolate the factors that indicate a claim or payment request has a high
probability of fraudulence?
Develop rules and use them to flag only those claims or requests most
likely to be fraudulent?
Ensure your adjusters could review claims or requests that are not only
likely to be fraudulent but also have the greatest adjustment potential?
If your company could accomplish these goals, you could use your
resources more efficiently and more effectively prevent and reduce
fraudulent activity. Then your department could reduce the substantial
amount of money lost to fraudulent claims each year.
Capitalize on existing data
Your previously audited claims hold the key to recouping money in the
future. By creating models from historical information, you can accurately
pinpoint fraudulent claims out of the millions of claims you receive
each year. These data mining models lower the cost of fraud and abuse
while saving your adjusters time. Data mining empowers a variety of
insurance providers with the ability to predict which claims are
fraudulent so they can effectively target their resources and recoup
significant amounts of money.
The following scenario demonstrates how one insurance provider – in
this instance a medical insurance provider – used IBM SPSS data
mining to build models based on previously audited claims to identify
potentially fraudulent claims. With these models in place, the provider’s
claim audit selection will be more exact, generate more money through
claim adjustments and save time and manpower.
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IBM Software
Business Analytics
IBM SPSS Modeler
Building models to find fraudulent claims
A large insurance provider needs to accurately determine which claims
are fraudulently filed so it can concentrate on preventing revenue loss.
Over the years, this organization collected audit results on insurance
claims. The organization seldom used historical records to identify
probable fraudulent claims in the future. Their previous methods often
missed opportunities to collect money and adjusters spent too much
time reviewing legitimate claims.
Data mining now enables this company to predict which insurance
claims are likely to be fraudulent. This gives adjusters the power to
determine what returns they should target, thereby, recouping millions of
dollars otherwise lost and saving adjusters many hours of valuable time.
The insurance company’s fraud detection office used IBM SPSS
Modeler, the leading data mining workbench, to get results. Modeler
examines each line entry on claims, compares the line entries against
the amount of fraud dollars detected, ranks claims in the order of likely
fraudulence and displays the results back.
The following scenario describes how the agency created models and
predicted which claims might be fraudulent.
Understand your data
IBM SPSS Modeler’s visual programming interface makes examining
and modeling the audit records straightforward. Records used to model
medical claims might include medical insurance billing records with
detailed information such as recipient/provider codes and county of
residence, diagnostic codes, admission source, length of stay and total
charges claimed.
Determine your population makeup
An important step in the data mining process is to continually ensure
you are using the right data to solve your business problem. You must
ensure data doesn’t disproportionately represent any provider or
exclusively associate any provider with one particular diagnostic code.
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IBM Software
Business Analytics
IBM SPSS Modeler
Discover relationships in your data
IBM SPSS Modeler easily adds new variables to each record in the
dataset. Then we can route data to a node that will build a web graphic
to examine how frequently (and for which diagnostic codes) each
provider filed claims for out-of-county services. This information might
prove useful further along in our analysis.
Build a model
In this step, we model the total charges on an insurance claim, using
the admissions source, length of stay and diagnostic codes as inputs. We
chose a modeling procedure called rule induction because it is easy to
understand. When we insert the model into the stream, the model will
read the inputs (admissions source, length of stay and diagnostic code)
for each record, and then produce a projected value for total charges.
We’ll use this new value later on.
Use the model against actual records
To examine the difference between the actual charges recorded on each
insurance claim and the charges that our model projected, we will graph
one against the other in a plot graphic. As part of the graphic, we could
add a graph of the line, y=x. If the actual charges equal the projected
charges on a particular record, that records point should fall on this
line. On the other hand, if the record’s actual charges were greater than
the model projected, its point would be above this line.
Segment your data
To further drill down on the differences between actual and projected
charges, we derive a new variable to add to our data. This new variable,
“miss,” is then graphed in a histogram. It shows that the majority of
records in our data had a miss value clustered very close to $0.
However, few records have a miss value that extends to $10,000 and
beyond. Now we can narrow our attention to only those latter records.
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IBM Software
Business Analytics
IBM SPSS Modeler
Compare your subset to the entire population
Suppose that two providers had disproportionately high claims values.
Exploring the types of claims submitted might uncover very suspicious
activity for one of the claimants. They filed claims exclusively for one
diagnostic code. The business analyst can easily identify this highly
suspicious behavior and investigate the claims. Data mining proved
useful for two reasons. First, it yielded valuable information about
potential cases of fraud in the records currently on file. Although by no
means an open-and-shut case of fraudulent claims submittal, the
evidence we gathered can now be passed to investigators.
Plus, the IBM SPSS data mining model we built can be applied to
future claims. The model will compute total projected charges on
incoming claims. These projections can be compared against actual
charges, and the system will “flag” questionable claims for investigation.
With the information uncovered through data mining, adjusters can
focus on claims that may yield larger adjustments, and are less likely to
waste time investigating legitimate claims. With data mining, your
adjusters can focus on recovering money so your organization’s bottom
line is less affected by fraud.
Although this insurance agency’s fraud detection office used data
mining for provider fraud, you could also use it for:
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Eligibility fraud
Auto insurance fraud
Credit fraud
Online fraud
As well as many other types of fraud
And, because circumstances change over time, you can periodically
review the models and update them so they continue to be effective
and deliver the best results. IBM SPSS Modeler is the only data mining
product that empowers organizations to continually modify the data
mining process to keep decision-makers updated.
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IBM Software
Business Analytics
IBM SPSS Modeler
Strategically deploy your data mining results
for optimum success
Once you have models that predict fraudulent activity, you need to
strategically deploy your results to the people who can use that
information to eradicate fraud and recoup money. Strategic deployment
means integrating models into your company’s daily operations. Strategic
deployment empowers you to put timely, consistent information into
the right hands. Everyone in your organization is on the same page and
can act more quickly to recoup the most money for your organization.
IBM® SPSS® Decision Management enables you to score values against
new claims and payment requests, then deploy your model and distribute
the results (for example, a list of claims most likely to be noncompliant). Depending on your needs, you can display these results
over an intranet, through email or via hard-copy reports.
For companies that have local or branch offices, strategic data mining
deployment provides an additional benefit. The central office can store
and mine data for the entire organization and deploy the data mining
results to local offices, which are often charged with stopping fraud and
abuse. Deploying data mining results to local offices can stretch scarce
resources, empowering you to consistently share information
throughout the organization.
Not sure where to start?
You can begin by tracking and solving critical business problems using
the best practice approach to data mining, CRoss-Industry Standard
Process for Data Mining (CRISP-DM). CRISP-DM is a comprehensive
data mining methodology and process model that makes large data
mining projects faster, more efficient and less costly. Our company
subscribes to this best practice approach to data mining – which it
co-authored with several other leading companies – and brings it to
your organization to deliver actionable results. The CRISP-DM model
offers step-by-step direction, tasks and objectives for every stage of the
process, including business understanding, data understanding, data
preparation, modeling, evaluation and deployment. This methodology
can provide an excellent starting point for your data mining efforts by
helping you:
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Assess and prioritize business issues
Articulate data mining methods to solve them
Apply data mining techniques
Interpret data mining results
Deploy and maintain data mining results
For more information about CRISP-DM, see www.crisp-dm.org.
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IBM Software
Business Analytics
IBM SPSS Modeler
Data mining makes the difference
Discover patterns that indicate which claims have a high probability of
fraudulence when you apply sophisticated data mining techniques to
your past claims data.
IBM SPSS solutions help you use data and technology better and
improve your ability to recoup significant amounts of money. Our
experts team with you to merge deep analytical and technical
knowledge with your business expertise. Along the way, they educate
your staff, provide recommendations and build a repeatable process so
your organization can apply the skills and tools to easily proceed on
your own.
Analyze your data using a variety of techniques, from simple reports to
advanced methods for predicting provider, client or vendor behavior.
It’s important to use multiple analytical methods so you can work with
many types of data in many applications and always get answers that
lead to the best chance to realize substantial claim adjustments.
About IBM Business Analytics
IBM Business Analytics software delivers complete, consistent and
accurate information that decision-makers trust to improve business
performance. A comprehensive portfolio of business intelligence,
predictive analytics, financial performance and strategy management,
and analytic applications provides clear, immediate and actionable
insights into current performance and the ability to predict future
outcomes. Combined with rich industry solutions, proven practices and
professional services, organizations of every size can drive the highest
productivity, confidently automate decisions and deliver better results.
As part of this portfolio, IBM SPSS Predictive Analytics software helps
organizations predict future events and proactively act upon that insight
to drive better business outcomes. Commercial, government and
academic customers worldwide rely on IBM SPSS technology as a
competitive advantage in attracting, retaining and growing customers,
while reducing fraud and mitigating risk. By incorporating IBM SPSS
software into their daily operations, organizations become predictive
enterprises – able to direct and automate decisions to meet business
goals and achieve measurable competitive advantage. For further
information or to reach a representative visit www.ibm.com/spss.
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May 2010
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